Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers
This paper introduces a method to integrate target behavior into the multiple hypothesis tracker (MHT) likelihood ratio. In particular, a periodic track appraisal based on behavior is introduced that uses elementary topological data analysis coupled with basic machine learning techniques. The track appraisal adjusts the traditional kinematic data association likelihood (i.e., track score) using an established formulation for classification-aided data association. The proposed method is tested and demonstrated on synthetic vehicular data representing an urban traffic scene generated by the Simulation of Urban Mobility package. The vehicles in the scene exhibit different driving behaviors. The proposed method distinguishes those behaviors and shows improved data association decisions relative to a conventional, kinematic MHT.
Duke Scholars
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Related Subject Headings
- 5102 Atomic, molecular and optical physics
- 4009 Electronics, sensors and digital hardware
- 4006 Communications engineering
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Related Subject Headings
- 5102 Atomic, molecular and optical physics
- 4009 Electronics, sensors and digital hardware
- 4006 Communications engineering